761 research outputs found
Multimedia information technology and the annotation of video
The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning
Information-Theoretic Active Learning for Content-Based Image Retrieval
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode
active learning method for binary classification, and apply it for acquiring
meaningful user feedback in the context of content-based image retrieval.
Instead of combining different heuristics such as uncertainty, diversity, or
density, our method is based on maximizing the mutual information between the
predicted relevance of the images and the expected user feedback regarding the
selected batch. We propose suitable approximations to this computationally
demanding problem and also integrate an explicit model of user behavior that
accounts for possible incorrect labels and unnameable instances. Furthermore,
our approach does not only take the structure of the data but also the expected
model output change caused by the user feedback into account. In contrast to
other methods, ITAL turns out to be highly flexible and provides
state-of-the-art performance across various datasets, such as MIRFLICKR and
ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages
appendix
Long-term Tracking in the Wild: A Benchmark
We introduce the OxUvA dataset and benchmark for evaluating single-object
tracking algorithms. Benchmarks have enabled great strides in the field of
object tracking by defining standardized evaluations on large sets of diverse
videos. However, these works have focused exclusively on sequences that are
just tens of seconds in length and in which the target is always visible.
Consequently, most researchers have designed methods tailored to this
"short-term" scenario, which is poorly representative of practitioners' needs.
Aiming to address this disparity, we compile a long-term, large-scale tracking
dataset of sequences with average length greater than two minutes and with
frequent target object disappearance. The OxUvA dataset is much larger than the
object tracking datasets of recent years: it comprises 366 sequences spanning
14 hours of video. We assess the performance of several algorithms, considering
both the ability to locate the target and to determine whether it is present or
absent. Our goal is to offer the community a large and diverse benchmark to
enable the design and evaluation of tracking methods ready to be used "in the
wild". The project website is http://oxuva.netComment: To appear at ECCV 201
The Visual Extent of an Object
The visual extent of an object reaches beyond the object itself. This is a long standing fact in psychology and is reflected in image retrieval techniques which aggregate statistics from the whole image in order to identify the object within. However, it is unclear to what degree and how the visual extent of an object affects classification performance. In this paper we investigate the visual extent of an object on the Pascal VOC dataset using a Bag-of-Words implementation with (colour) SIFT descriptors. Our analysis is performed from two angles. (a) Not knowing the object location, we determine where in the image the support for object classification resides. We call this the normal situation. (b) Assuming that the object location is known, we evaluate the relative potential of the object and its surround, and of the object border and object interior. We call this the ideal situation. Our most important discoveries are: (i) Surroundings can adequately distinguish between groups of classes: furniture, animals, and land-vehicles. For distinguishing categories within one group the surroundings become a source of confusion. (ii) The physically rigid plane, bike, bus, car, and train classes are recognised by interior boundaries and shape, not by texture. The non-rigid animals dog, cat, cow, and sheep are recognised primarily by texture, i.e. fur, as their projected shape varies greatly. (iii) We confirm an early observation from human psychology (Biederman in Perceptual Organization, pp. 213-263, 1981): in the ideal situation with known object locations, recognition is no longer improved by considering surroundings. In contrast, in the normal situation with unknown object locations, the surroundings significantly contribute to the recognition of most classes
The landscape of multimedia ontologies in the last decade
Many efforts have been made in the area of multimedia to bridge the socalled “semantic-gap” with the implementation of ontologies from 2001 to the present. In this paper, we provide a comparative study of the most well-known ontologies related to multimedia aspects. This comparative study has been done based on a framework proposed in this paper and called FRAMECOMMON. This framework takes into account process-oriented dimension, such as the methodological one, and outcome-oriented dimensions, like multimedia aspects, understandability, and evaluation criteria. Finally, we derive some conclusions concerning this one decade state-of-art in multimedia ontologies
High-level feature detection from video in TRECVid: a 5-year retrospective of achievements
Successful and effective content-based access to digital
video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like
colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip.
The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work
done on the TRECVid high-level feature task, showing the
progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can
achieve large-scale, fast and reliable high-level feature detection on video
Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion
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